6 Genomic and Proteomic Analysis of Wine Yeasts Jose´ E. Pe´rez-Ortı´n, Jose´ Garcı´a-Martı´nez

C H A P T E R
6
Genomic and Proteomic Analysis
of Wine Yeasts
José E. Pérez-Ortı´n, José Garcı´a-Martı´nez
Departamento de Bioquı́mica & Biologı́a Molecular & Laboratorio de Chips de DNA, SCSIE,
Universitat de València, Spain
O U T L I N E
1. Introduction
143
4.2. Effects of Drugs and Other External
Factors
4.3. Use of DNA Microarrays in the
Analysis of Wine Yeasts
4.4. Genomic Studies
2. Genomic Characteristics of Wine Yeasts 144
3. Comparative Genomics and the Origin
of the S. Cerevisiae Genome
147
4. The Use of S. Cerevisiae as a Model
Organism for the Development of DNA
Microarray Technology
150
4.1. Metabolic Studies
150
1. INTRODUCTION
The selection of suitable microorganisms for
use in industrial processes is a key issue in
food biotechnology. One of the key challenges
in this area is to improve the properties of starter
cultures, such as the ability to establish reproducible growth. Many of the programs aimed
at enhancing the properties of industrial
Molecular Wine Microbiology Doi: 10.1016/B978-0-12-375021-1.10006-2
152
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157
5. Proteomic Analysis of Wine Strains
158
6. Other Global Studies
161
7. Future Directions
163
microorganisms, however, are restrained by
a lack of sufficient knowledge regarding the
metabolic and regulatory processes occurring
within the cells. These shortcomings may,
however, be short-lived, considering the continuous advances being made in functional genomics and proteomics. Studies in these areas
will help, for example, to identify the effects of
genetic alterations on final products, generate
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Copyright Ó 2011 Elsevier Inc. All rights reserved.
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6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
desirable pleiotropic effects through mutations
in regulatory genes, predict stress responses in
the different environments to which microorganisms are exposed, and identify genomic
variations associated with adaptation to the
particular conditions of winemaking.
This chapter focuses exclusively on the yeast
species Saccharomyces cerevisiae. In addition to
being the main microorganism involved in
wine fermentation, it has been used as a model
organism in molecular biology for many years
(Miklos & Rubin, 1996) and is the only wine
yeast species for which abundant genomic and
proteomic information is available. It was the
first eukaryote to have its complete genome
sequenced (Goffeau et al., 1997), and, since
then, numerous functional analysis projects
have uncovered enormous amounts of information on the biology of this microorganism
(Dujon, 1998). It can safely be said that S. cerevisiae is currently the best understood of all
eukaryotic organisms. Most of the techniques
currently used in functional genomics and proteomics were initially developed in this yeast.
DNA chip, or microarray, technology, for
example, was primarily developed using S. cerevisiae (DeRisi et al., 1997; Schena et al., 1995;
Wodicka et al., 1997), and all the latest advances
in this field have also been tested using this
yeast (see Section 4). Vast amounts of data
have thus been compiled on gene expression
in S. cerevisiae. Indeed, the information on S. cerevisiae far exceeds that available for any other
prokaryotic or eukaryotic organisms. As a result,
it has been possible to propose global models for
genetic and metabolic regulation (Gasch et al.,
2000).
The fact that S. cerevisiae was the first microorganism to be widely used in the development of
genome technology allowed other phylogenetically related yeasts to be analyzed subsequently
in global sequencing projects, and the use of
comparative genomics has since led to important conclusions regarding gene functionality
(Butler et al., 2009; Cliften et al., 2003; Kellis
et al., 2003; Liti et al., 2009; Souciet et al., 2000).
DNA microarray analysis is a very useful tool
for comparing genomes from different strains
of S. cerevisiae, including wine strains (Carro
et al., 2003; Hauser et al., 2001; Schacheter
et al., 2009) and similar species.
S. cerevisiae has also been used in the development of the more recent field of proteomics.
Proteomic studies have generated vast amounts
of data on protein expression profiles and
variability in laboratory strains of S. cerevisiae
(Washburn et al., 2001), and these have
recently been extended to include wine strains
(Rossignol et al., 2009; Trabalzini et al., 2003;
Zuzuárregui et al., 2006). Important advances
have also been made in metabolomics, a new
field in which S. cerevisiae is practically the
only eukaryote to have been studied to date
(Raamsdonk et al., 2001; Rossouw et al., 2008).
The integration of different types of “omic” data
into predictive models has provided the basis
for new research strategies in systems biology
(Borneman et al., 2007; Pizarro et al., 2007).
Most of the information that has been gathered in all of the above areas is related to laboratory strains of S. cerevisiae, although more recent
studies have been extended to other strains
(particularly wine strains) and industrial processes
(Bisson et al., 2007). Knowledge generated
from the analysis of laboratory strains may be
helpful in understanding the results of studies
conducted with wine strains during industrial
fermentation, and it is extremely simple to
apply techniques used with laboratory strains
to their industrial counterparts. This chapter
will therefore also look at the methods used
and results obtained for non-wine strains of
S. cerevisiae.
2. GENOMIC CHARACTERISTICS
OF WINE YEASTS
The history of wine yeasts is as old as the
earliest civilizations in the Mediterranean
GENOMIC CHARACTERISTICS OF WINE YEASTS
region, with the first references to winemaking
dating back to 7400 years ago. Reports of wine
production were limited to this geographical
area for many centuries, until the practice was
spread to other parts of the world with suitable
climate conditions, as Europe embarked on its
conquest of other continents in the fifteenth
century (reviewed in Mortimer, 2000 and Pretorius, 2000). Must fermentation was considered to occur spontaneously until 1863, when
Louis Pasteur discovered that yeasts were
responsible for the process. Although numerous
yeasts and bacteria contribute to must fermentation (see Chapters 2e6 and 9), the principle
microorganisms responsible for this biotransformation belong to the genus Saccharomyces,
principally S. cerevisiae. This is why S. cerevisiae
is often referred to as the wine yeast (Pretorius,
2000).
The origin of S. cerevisiae has been much
debated. While some authors are of the opinion
that it is naturally present in fruit (Mortimer &
Polsinelli, 1999), others believe that its origin is
more recent and that it is the result of hybridization with other natural species and subsequent
natural selection in artificial environments
(Martini, 1993). This second hypothesis is supported by the fact that S. cerevisiae is found
only in areas close to human activity. According
to this theory, all the modern isolates of S. cerevisiae would have been transported by insects
from the winery back to the vineyards (Naumov,
1996). While this debate is central to determining
the true origin of the S. cerevisiae genome, what is
known for certain is that the genomic constitution of this species has been molded by the
severe fermentation-related stresses to which
it has been exposed throughout the centuries.
Proof of this are the genomic differences
between primary and secondary fermentation
wine strains and between brewing strains and
bread-making strains, whose genotypes have
been unknowingly selected over hundreds of
years with the continual improvements made
to these biotechnological processes. Another
145
important point is that all of today’s laboratory
strains are derived from natural isolates. The
best-documented case is that of the most
popular yeast among molecular biologists: the
S288c strain, which was derived from a heterothallic (ho), diploid strain isolated in a rotten
fig in California in 1938 (Mortimer & Johnston,
1986). It is very likely that the strain had been
transported from a winery by insects.
Most laboratory strains of S. cerevisiae are ho,
haploid or diploid, and have a set of 16 fixedlength chromosomes (see Figure 6.1). The
majority of wine strains, in contrast, are diploid,
aneuploid, or polyploid (Bakalinsky & Snow,
1990; Codón et al., 1995). They are also homothallic (HO), variably heterozygous (Barre
et al., 1993; Butler et al., 2009; Carreto et al.,
2008; Codón et al., 1995), and characterized by
a high level of polymorphism in chromosome
length (Bidenne et al., 1992; Rachidi et al.,
1999). Many strains are trisomic or tetrasomic
for certain chromosomes (Guijo et al., 1997;
Bakalinsky & Snow, 1990). The above characteristics have numerous practical implications,
including highly variable sporulation capacity
(0e75%) (Bakalinsky & Snow, 1990; Barre
et al., 1993; Mortimer et al., 1994) and spore
viability (0e98%) (Barre et al., 1993; Codón
et al., 1995; Mortimer et al., 1994). The ability
of S. cerevisiae to alter its genome is enhanced
by the existence of mitotic and meiotic cycles.
Genome ploidy and plasticity provide wine
yeasts with certain advantages that facilitate
their adaptation to changing external environments and perhaps also increase the dosage of
genes that have an important role in fermentation (Bakalinsky & Snow, 1990; Salmon, 1997).
This genomic plasticity, however, is not
restricted to S. cerevisiae and even allows stable
hybridization with closely related species.
Several natural strains, such as S6U and CD1,
for example, are hybrids of S. cerevisiae and
Saccharomyces bayanus. S6U is an allotetraploid
(Naumov et al., 2000), which probably explains
its stability despite having two distinct
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6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
(a)
VIII
XVI
(b)
IV
X
FIGURE 6.1 The genome of the reference Saccharomyces
cerevisiae laboratory strain has 16 chromosomes whose lengths
are shown to scale (a). The centromeres are shown as white
dots. The haploid genome is shown in this figure. Diploid
strains have two, probably identical, copies of each chromosome. Many variants of this reference genome have been
found in wine strains. The T73 strain (b), for example, isolated
in musts from the Alicante appellation (Querol et al., 1992) has,
at least, the following variations: (1) a reciprocal translocation
between chromosomes VIII and XVI, which generates two
variants of each chromosome in T73 (Pérez-Ortı́n et al., 2002a)
(the site of the translocation is shown by grey arrows) (a); (2) an
additional, presumably identical, copy of chromosomes IVand
X (Pérez-Ortı́n, unpublished results); (3) many variations in
the copy number of genes from subtelomeric families, shown
by arrowheads (b) (Garcı́a-Martı́nez & Pérez-Ortı́n, unpublished results); and (4) markedly fewer copies of Ty transposons (Hauser et al., 2001). The T73 genome shown probably
has two copies of each chromosome except for chromosomes
IV and X. For simplicity, we have shown just a single copy of
chromosomes with two copies. For chromosomes with three
copies, we show the name and just two copies. We have
included the two copies of chromosomes VIII and XVI to show
the translocation between these chromosomes.
genomes. The same has been observed with
brewing strains (Kielland-Brandt et al., 1995).
The formation of interspecific hybrids between
members of the Saccharomyces sensu stricto
group appears to be one of the adaptive mechanisms employed by industrial yeasts (Belloch
et al., 2009; Querol et al., 2003). This genome
plasticity, which is inherent in wine strains, is
not a desirable property in model organisms
used in genetic studies, and laboratory strains
used for such purposes are selected precisely
for their lack of plasticity. Laboratory strains
are also capable of adapting to changing environmental conditions, normally via point mutations (Ferea et al., 1999), although in certain
circumstances large regions or entire chromosomes may also be modified (Hughes et al.,
2000b).
Wine strains, unlike laboratory strains, are
capable of chromosomal rearrangement during
mitosis (Longo & Vézinhet, 1993). In an experiment by Puig et al. (2000), URA3 was replaced
with an exogenous marker gene, KanMX, in
the natural wine strain T73 and used to monitor
genetic variation in a series of consecutive must
fermentations. The authors found that URA3
homozygotes appeared at a rate of 2 105
per generation in a process they attributed to
mitotic recombination or gene conversion.
Phenotypically, the Ura cells were at a selective
disadvantage to the Uraþ cells (heterozygotes
[URA3/ura3] and homozygotes [URA3/
URA3]). Chromosomal changes were also
detected in some cells. Because of their strong
tendency towards genomic changes, wine
strains do not display the same genetic uniformity as that used to define laboratory strains
(Pretorius, 2000; Snow, 1983). This problem is
further compounded by the HO nature of these
strains. Haploid cells produced by sporulation
can change their mating type and conjugate to
form new diploid cells. The frequent use of
such mechanisms during vinification would
lead to the generation of multiple genome
combinations and very rapid changes. This
COMPARATIVE GENOMICS AND THE ORIGIN OF THE S. CEREVISIAE GENOME
particular evolutionary mechanism has been
termed “genome renewal” (Mortimer et al.,
1994; Mortimer, 2000). The proponents of this
theory suggest that this renewal would give
rise to highly homozygous strains and eliminate
deleterious mutations by natural selection.
Natural strains are known, however, to be typically aneuploid (Bakalinsky & Snow, 1990; Guijo
et al., 1997) and heterozygous for many loci
(Barre et al., 1993; Kunkee & Bisson, 1993), and
such properties are inconsistent with the
genome renewal hypothesis (Puig et al., 2000).
While the possible influence of meiotic changes
cannot be entirely ruled out, there are other
mechanisms that might explain the natural variation observed in wine strains. For instance,
translocations mediated by Ty transposons
(Rachidi et al., 1999), mitotic crossing-over
(Aguilera et al., 2000), and gene conversion
have all been described as mechanisms capable
of causing the most rapid adaptive changes
(Puig et al., 2000).
The practice of inoculating must with pure
wine yeast cultures to improve the quality and
homogeneity of wines produced from one year
to the next dates back to the 1970s (Pretorius,
2000). Pure cultures have been obtained from
natural strains in wine-producing countries
around the world. In the first half of the twentieth century, these strains were selected and
modified by more or less empirical methods.
The selection techniques were improved in later
years, however, with the emergence of classical
genetic tools (reviewed in Pretorius, 2000). The
end of the twentieth century brought genetic
engineering methods that opened up a world
of possibilities and further improved the quality
of the selection methods used (see Chapter 8).
The plasticity of the wine strain genome,
however, poses a new challenge, as there is a
risk of genetically engineered changes becoming
unstable with successive generations. Mutations
or insertions in a single locus, for example,
could eventually be eliminated by gene conversion, homologous recombination, or even perhaps
147
by meiosis and conjugation. Consequently, all
the homologous loci in a particular strain (two
or more, depending on the case) must be manipulated in an identical fashion to ensure the
phenotypic stability of the strain (Puig et al.,
1998, 2000).
3. COMPARATIVE GENOMICS AND
THE ORIGIN OF THE S. CEREVISIAE
GENOME
Although the origin of S. cerevisiae is
unknown, that of its genome can be investigated
by comparing genomes from natural strains of
this species with those from other more-orless-related species. A better understanding of
the origin and evolution of the S. cerevisiae
genome will have a positive impact in
numerous areas. It will greatly improve our
knowledge of the origin of the species and the
ways in which it has adapted to industrial
processes over the years, and also shed light
on the mechanisms underlying the evolution
of its genome, and, by extension, that of other
eukaryotic organisms.
Comparative genomics studies in yeasts have
been performed by partial or complete
sequencing followed by bioinformatic comparison of sequence data and chromosomal organization of genes. The first complete genome
sequence for S. cerevisiae was published for
a laboratory strain in 1997 (Goffeau et al.,
1997). The corresponding sequences for natural
wine strains were made available about 12 years
later (Borneman et al., 2008; Novo et al., 2009).
Today, full genome sequences are available for
several dozen S. cerevisiae strains, including
laboratory, wine, and other strains (Liti et al.,
2009; Schacherer et al., 2009).
In 1997, it was suggested that the S. cerevisiae
genome was the result of an ancient duplication,
dating back approximately 108 years, of an
ancestral genome followed by the elimination
of duplicated genes and the acquisition of new
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6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
functions for other genes (Wolfe & Shields,
1997). This theory would explain the genetic
redundancy detected in this species. S. cerevisiae
has 2458 genes from 722 families containing
between two and 108 members (Herrero et al.,
2003). Part of the redundancy would be due to
ancestral duplication and part to smaller duplications that took place later (Llorente et al.,
2000). The existence of large numbers of gene
families is a common feature of hemiascomycetous yeasts. In a comparative genomic study of
these yeasts, Malpertuy et al. (2000) found
a substantial number of genes that do not exist
in other organisms. The genes, which are
specific to ascomycetes, seem to have evolved
more rapidly and are perhaps responsible for
the biological differences that characterize this
group of yeasts. When this ancient duplication
actually took place in S. cerevisiae is a subject
of debate. Langkjaer et al. (2003) postulated
that it was before the divergence of Saccharomyces and Kluyveromyces but other authors
have suggested that it was later (Fares & Wolfe,
2003). In a study of collinearity (synteny)
between different hemiascomycete species,
Llorente et al. (2000) proposed that the primary
evolutionary mechanism (apart from global
genome duplication) was the duplication of
small regions (the length of a few genes) of the
genome followed by specialization or gene
loss. In related species, such as S. cerevisiae and
S. bayanus, the duplication sites tend to be
located close to copies of Ty transposons or in
subtelomeric regions where families of repeated
genes are concentrated (Fischer et al., 2001). A
genomic comparison of S. cerevisiae, Saccharomyces paradoxus, S. bayanus, and Saccharomyces
mikatae found the greatest variability in subtelomeric regions, particularly in terms of repeated
gene families (Kellis et al., 2003). These regions
range in size from 7 to 52 kb and their function
might be to facilitate rapid changes via duplication and translocation. While these mechanisms
have played a part in the evolution of the
Saccharomyces genus, they have also had
a much more recent role in facilitating adaptation to specific industrial processes. Indeed,
various subtelomeric gene families are of
immense importance to the biology of these
yeast strains. Based on the results of a comparative genomic study of multiple wine and nonwine strains, Carreto et al. (2008) proposed
that the diversity observed in the strains
analyzed was mainly the result of Ty element
insertions and subtelomeric recombination.
The fact that the subtelomeric regions of
different chromosomes contain many members
of gene families involved in hexose transport
(Bargues et al., 1996), use of natural carbon sources such as sucrose (Carlson et al., 1989),
maltose (Chow et al., 1989), and melibiose (Naumova et al., 1997), flocculation (Teunissen &
Steensma, 1995), and resistance to the toxicity
of molasses in which industrial yeasts are
cultured (Ness & Aigle, 1995) suggests that
these regions might act as reservoirs of variability for rapid adaptations to the changing
environments to which industrial yeasts are
exposed. This mechanism may indeed still be
very active in certain strains such as Cava
strains, in which high rates of subtelomeric variability have been detected (Carro et al., 2003;
Carro & Piña, 2001). Small and large duplications and translocations may also have contributed to speciation due to reproductive isolation
in the Saccharomyces genus (Delneri et al., 2003;
Fischer et al., 2000, 2001). There may be other
cases where the selection of one particular chromosomal rearrangement rather than another is
random. Nonetheless, it is reasonable to think
that many of the combinations produced by
the different genomic rearrangement mechanisms discussed above have been selected
because they provide the organism with a particular selective advantage. Our group found
a case in which reciprocal translocation between
chromosomes VIII and XVI gave rise to a new,
more efficient promoter for the sulfite resistance
gene SSU1 (Pérez-Ortı́n et al., 2002a). As sulfite
has been used as a treatment in vineyards,
COMPARATIVE GENOMICS AND THE ORIGIN OF THE S. CEREVISIAE GENOME
wineries, and wines for thousands of years,
resistance to this substance was probably
selected by wine strains as a useful survival
mechanism. In an extensive study of translocation between various wine and non-wine
strains, our group found that the reciprocal
translocation between chromosomes VIII and
XVI was present in some but not all of the
wine strains, but was absent from all the nonwine strains, providing evidence that this translocation is associated with the use of sulfite in
winemaking (Pérez-Ortı́n et al., 2002a). In that
study, we also detected a close phylogenetic
relationship between wine strains from
geographically distant countries such as South
Africa, France, Japan, Spain, and the United
States, suggesting that strains that had originated in Europe were spread to other parts of
the world with the expansion of winemaking.
The recent development of high-resolution
genome mapping techniques such as mass
sequencing and tiling array analysis (see Section
4) has permitted the genomic sequencing of
several dozen S. cerevisiae strains and the formulation of hypotheses regarding the origin of this
species and that of other strains used for
biotechnological purposes (brewing, bread
making, sake production) and pathogenic
strains isolated in immunosuppressed patients
(Liti et al., 2009; Schacherer et al., 2009). Single
nucleotide polymorphism (SNP) analysis has
shown that the genomes of different strains of
S. cerevisiae tend to represent a mosaic generated
by recombination between lineages with
different geographical and/or ecological origins
(Liti et al., 2009). What seems clear is that this
species has been domesticated on various separate occasions, at least once in the case of wine
fermentation and another time in the case of
sake fermentation (Liti et al., 2009). Today’s
strains would thus be derivatives and combinations of those initial domesticated strains. Pathogenic strains, however, seem to have arisen on
multiple occasions from wild and domesticated
strains opportunistically adapted to the new
149
ecosystem of human tissues (Schacherer et al.,
2009).
Another interesting point worth noting is the
discovery of hybrid wine yeasts derived from S.
cerevisiae and other Saccharomyces species. It has
been known for some time that certain lager
brewing strains have genomes derived from
more than one species (Rainieri et al., 2006).
These strains are partial allotetraploids that
arose from a natural hybridization event
between S. cerevisiae and a yeast similar to S.
bayanus (Nakao et al., 2009; Rainieri et al.,
2006). More recently, however, there have also
been descriptions of wine strains with a genome
containing chromosomes from more than one
species and wine yeast hybrids of S. bayanus
and Saccharomyces kudriavzevii (González et al.,
2006). Genomic analysis showed that all the
hybrids arose from a single hybridization event.
The resulting genome would then have evolved
through successive chromosome rearrangements resulting in the generation of hybrid
chromosomes and the loss of several chromosome copies (mostly corresponding to S.
kudriavzevii). Such rearrangements affected not
only sequences of transposons (as in the cases
described above) but also other conserved
regions such as ribosomal DNA (rDNA) and
protein-encoding genes (Belloch et al., 2009).
The study of these hybrids is of practical
interest because they might have useful properties for biotechnological applications. It is
known, for example, that S. bayanus var. uvarum
is responsible for the fermentation of must at
low temperatures and the production of large
quantities of glycerol and b-phenylethanol (Solieri et al., 2008). In an attempt to obtain yeast
strains with improved winemaking properties,
Solieri et al. (2008) constructed artificial hybrids
between S. cerevisiae and Saccharomyces uvarum
by spore conjugation and found that the
hybrids contained mitochondria from only one
of the two species and that the fermentative
properties of the hybrid depended on these
mitochondria.
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6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
4. THE USE OF S. CEREVISIAE AS
A MODEL ORGANISM FOR THE
DEVELOPMENT OF DNA
MICROARRAY TECHNOLOGY
There are a number of reasons why many of
the technologies used in the field of genomics
were developed using S. cerevisiae, but the main
one is probably that it was the first organism to
be analyzed in a genomic sequencing project
that generated numerous functional genomics
studies even before the full sequence was published (Goffeau et al., 1997). The fact that S. cerevisiae has been used as a model organism for
genetics and molecular biology since the 1940s
has given rise to an enormous number of very
powerful tools for these types of analysis. As
a result of these developments, our knowledge
of the genetics and biology of this yeast is unparalleled. The only other organism that has been so
thoroughly investigated is perhaps Escherichia
coli. Even before the emergence of DNA microarray technology, S. cerevisiae was used in the
development of numerous methods for the
global analysis of gene expression such as Serial
Analysis of Gene Expression (SAGE) technology,
which was used to perform the first analysis of
the entire messenger RNA (mRNA) complement
(baptized transcriptome) of a cell (Velculescu
et al., 1997). As with many other technologies,
SAGE was later used to analyze other organisms
with great success (Velculescu et al., 2000). While
SAGE is an extremely powerful tool, capable of
accurately quantifying the number of copies of
mRNA present in a cell, it has largely been
replaced by DNA microarray analysis, which is
a much simpler and less costly technology. In
recent years, however, the development of
high-throughput sequencing techniques (also
developed using S. cerevisiae) has led to
a renewed interest in tag-sequencing technologies. RNA-seq, for example, has been successfully used to characterize the transcriptome of
S. cerevisiae with considerable improvements
over previous techniques in terms of sensitivity,
transcript quantification, and, to some degree,
resolution (Nagalakshmi et al., 2008).
DNA microarrays have been widely used to
investigate many aspects of S. cerevisiae metabolism (Figure 6.2). The technology has other uses,
however. Apart from providing valuable information on metabolic activity in different conditions and mutants, it has also been used to
investigate the effects of many drugs and toxic
products on gene expression and to analyze
genomic variations in S. cerevisiae and related
species. All of these uses have also been applied
to wine yeast strains.
4.1. Metabolic Studies
Given the vast information already available
on yeast regulatory pathways, global expression
studies should be able to provide sufficient data
to allow individual genes to be linked to one or
more phenotypes or metabolic pathways. It
should also theoretically be possible to determine the components of each of these pathways,
to provide, for the first time, a global view of
a eukaryotic cell. The first global gene expression study, performed by Pat Brown’s group,
used DNA microarray analysis to study gene
expression in S. cerevisiae during growth in
glucose and during the shift from fermentative
to respiratory growth (DeRisi et al., 1997). The
study has already become a classic in its field
and has been cited over 2500 times (as of August
2009). Similar studies have analyzed other
processes or situations that involve metabolic
changes. Transcriptional changes in S. cerevisiae,
for example, have been analyzed in the change
from a fermentable to a nonfermentable carbon
source (Kuhn et al., 2001), in aerobic compared
to anaerobic conditions in a continuous-culture
study (ter Linde et al., 1999), in the lag phase
prior to active culture growth (Brejning et al.,
2003), during sporulation (Chu et al., 1998),
and during the cell cycle (Cho et al., 1998).
Another major research focus is the functional
THE USE OF S. CEREVISIAE AS A MODEL ORGANISM FOR THE DEVELOPMENT
Control sample
151
Test sample
1. DNA or RNA
extraction
2. Labeling with
fluorescent dyes
or radioactive isotopes
3. Hybridization
4.
Arrays
5. Laser scanning
Control image
Test image
6. Image analysis and quantification
Control data
Test data
7. Comparison
8. Diagnosis
FIGURE 6.2 DNA microarray analysis. 1. RNA or DNA is extracted from a test and a control sample using conventional
methods. 2. In the case of microarrays on glass slides, the probes are labeled with fluorescent dyes (using a different fluorophore for the test and control sample). The probes used in macroarrays on nylon filters are labeled with radioactive
isotopes. 3. Just one hybridization step is used in glass-slide microarrays as these involve the use of a single array with both
the test and control samples mixed together prior to hybridization. Two hybridization steps are required for nylon-filter
macroarrays. These steps are preferably performed on the same filter but they need to be sequential as the probes are labeled
with radioactive isotopes. 4. Following hybridization, the arrays are washed to allow detection of the different hybridization
signals. 5. The hybridization images are captured using a laser scanner. This is done directly using two different lasers (one
for each fluorophore) in the case of microarray analysis. In macroarray analysis, however, latent images are generated on
special screens and later scanned by laser. 6. The readings generate an image for each sample. The intensity is then
quantified using special software that generates hybridization intensity data that allows comparison of the samples. 7.
Statistically significant differences are analyzed using purpose-designed programs. 8. The final stage involves the formulation of corresponding hypotheses and conclusions.
analysis of transcription factors via overexpression or analysis of null or conditional mutants
(Carmel-Harel et al., 2001; DeRisi et al., 1997;
Holstege et al., 1998).
Clusters of genes that display identical or
similar expression patterns under the different
conditions studied have been used to identify
the functions of individual genes based on the
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6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
assumption that coregulated genes must be
involved in the same metabolic pathways. The
most common way to conduct a study of this
type is to use clustering algorithms to group
genes by expression profiles (reviewed in
Hughes & Shoemaker, 2001 and Brazma &
Vilo, 2000) in order to identify groups that
have putative functional relationships. Another
way is to search for transcription-factor-binding
sites in gene promoters. Two types of study
have been used for this purpose: in silico
comparison of promoter sequences (Brazma
et al., 1998; Bussemaker et al., 2000; Hampson
et al., 2000; Roth et al., 1998) and in vivo studies
of genome-wide transcription-factor-binding
sites using a technique called Chip-ChIP, which
is a combination of DNA microarray analysis
(Chip) and chromatin immunoprecipitation
(ChIP).
4.2. Effects of Drugs and Other
External Factors
DNA microarray technology can be used to
measure, in a single experiment, an organism’s
global transcriptional response to treatment
with an external factor such as a drug or environmental agent (Gasch et al., 2000; Hughes
et al., 2000a; Jelinsky et al., 2000; Jelinsky &
Samson, 1999). Because the response of genes
to experimental conditions is a dynamic process
characterized by multiple interactions, analyzing
responses to external agents can reveal functional relationships within or between metabolic
pathways. Such techniques have been used to
analyze, for example, the transcriptional
response to inhibition of translation or amino
acid biosynthesis, or to compounds with antifungal activity (Bammert & Fosel, 2000; Hardwick et al., 1999; Jia et al., 2000). Molecular
targets of specific drugs can also be identified
by comparing expression profiles induced
by a particular drug with those induced in
mutants for specific genes (Hughes et al.,
2000a). Similar results can be achieved by
inducing haploinsufficiency, which consists of
studying growth deficiencies caused by the
loss of one of the two gene copies in a diploid
cell. To perform a systematic, comparative
study, it is necessary to have a full collection of
a diploid strain in which each gene has been
deleted and replaced with a specific sequence
tag (Winzeler et al., 1999). In these studies, the
full collection of approximately 6000 strains
with single deletions is grown together under
particular conditions (such as the presence of
a drug) and strains that exhibit delayed growth
compared to wild-type strains indicate genes
that are necessary for resistance to certain drugs
or culture conditions (Giaever et al., 1999, 2002).
This technique can uncover subtle growth
differences that would otherwise remain undetected. Up to 6000 strains can be compared
simultaneously thanks to the sequence tags
present in each strain, which enable an accurate
count to be made of the cells in a strain at any
moment using special DNA microarrays containing probes for each sequence tag. These
studies open new perspectives not only for
pharmacogenomics but also for the study of
the effect on wine yeasts of toxic substances
such as alcohol, pesticides, and treatments
such as copper and sulfite. Although most of
the studies to date have been conducted using
standard laboratory strains, the results can be
easily extrapolated to industrial strains.
The fermentation of sugars by wine yeasts is
followed by rapid growth and carbon dioxide
production, which can be interrupted with the
depletion of carbon or nitrogen sources or the
appearance of growth inhibitors (reviewed in
Pretorius, 2000). An improved understanding
of the metabolic changes that occur in the shift
from one carbon source to another (DeRisi
et al., 1997; ter Linde et al., 1999) and of metabolic signal transduction pathways (Hardwick
et al., 1999; Ogawa et al., 2000) will contribute
to improving the technical aspects of fermentation processes in wineries and help to prevent
stuck fermentations.
THE USE OF S. CEREVISIAE AS A MODEL ORGANISM FOR THE DEVELOPMENT
In an extensive large-scale experiment that
analyzed response to many of the stress conditions to which yeasts are exposed, Gasch et al.
(2000) found that the transcriptional response
to almost all of the stress factors tested was
practically identical across a large group of
genes. The authors termed this the “environmental stress response” (ESR). The experiment
provided a basis for further tests with wine
strains exposed to general or specific stresses
associated with wine fermentation. Indeed, the
first experiments of this type have already
been performed (see below). The ultimate aim
of such studies is to identify the most suitable
strains for the various fermentation conditions
found in different wineries and wines. Similar
experiments involving a limited group of genes
have also been performed (Ivorra et al., 1999). A
more detailed discussion is given in Chapter 2
of this book. On investigating the effect of
ethanol on laboratory yeast strains by DNA
microarray analysis, Alexandre et al. (2001)
concluded that cells used ionic homeostasis,
heat protection, and antioxidant defense, in
addition to previously described mechanisms,
to respond to stress. In a study of the effect of
copper excess and deficiency on laboratory
strains, also using DNA microarrays, Gross
et al. (2000) found that a small number of genes
were differentially expressed and that some of
these were involved in the iron uptake system.
This finding suggests that the copper and iron
uptake systems might be related. Because
copper is commonly used to inhibit bacterial
and fungal growth in wines, wine yeast strains
must be able to endure elevated copper concentrations and it would be useful to determine
how they have achieved this capacity.
4.3. Use of DNA Microarrays in the
Analysis of Wine Yeasts
S. cerevisiae was also the first microorganism
in which genomic tools such as DNA microarray analysis were used to analyze natural and
153
industrial strains. Since this yeast plays a key
role in winemaking and has an enormous influence on the final product, it is important to
understand the molecular events underlying
fermentation and the influence of the winery
and vintage, and of the physical, biological,
and chemical properties of the must, on this
process. Such an understanding would be
greatly enhanced by analysis of the gene expression profiles of these yeasts in different growth
conditions. Before the emergence of DNA
microarray technology, the expression profiles
of only a small number of genes at a time could
be analyzed in wine yeasts (see Chapter 2). Most
of the DNA microarray experiments described
so far in this chapter, however, have analyzed
laboratory strains, which are incapable of wine
fermentation.
Various strategies have been employed in
studies using DNA microarrays to analyze
expression profiles in wine yeasts. Two studies
have been conducted using laboratory media
and culture conditions (Cavalieri et al., 2000;
Hauser et al., 2001), whereas others have used
synthetic musts that reproduce the conditions
found in a natural environment but provide
the means to accurately determine and reproduce the composition of the must (Backhus
et al., 2001; Rossignol et al., 2003). Another
strategy has involved the use of grape juice
medium sterilized by filtration (Marks et al.,
2003; Mendes-Ferreira et al., 2007a, 2007b).
The use of standard laboratory conditions has
the advantage of allowing comparison of data
from wine strains with those from the more
extensively studied laboratory strains. The enormous amounts of information available on reference strains can thus be used to undertake
a much more in-depth investigation of the metabolic pathways and molecular mechanisms
underlying wine yeast fermentation. Cavalieri
et al. (2000), for example, detected at least twofold variability in global expression levels for
6% of the genome between progeny of a natural
wine strain isolate. Their findings indicate that
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6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
wine strains are highly heterozygous. Because
most of the metabolic differences segregated as
a suite of traits, the authors concluded that they
were the result of changes in a small number of
regulatory genes. One specific example would
be the genes involved in the biosynthesis of
amino acids. There have also been descriptions
of other phenotypes caused by changes in structural rather than regulatory genes, explaining
why these changes are not associated with other
phenotypes. Examples include the YHB1 gene
(Hauser et al., 2001), genes involved in resistance to sulfite (Pérez-Ortı́n et al., 2002a) and
copper, and the filigreed phenotype (Cavalieri
et al., 2000).
It is important to conduct experiments in reallife conditions as, although laboratory culture
conditions greatly facilitate analysis, they do not
fully reproduce the conditions found in natural
environments. Given the variability of natural
musts, one option is to use synthetic musts,
which mimic natural conditions but can be
easily reproduced in different laboratories. In
a study of this type, using macroarrays and
various wine strains with different fermentative
capacity, Zuzuárregui and del Olmo (2004)
found that the expression levels of certain
stress-response genes were similar across the
strains. They also found that the mRNA levels
of many of these genes remained very high in
the strains with weaker fermentative capacity.
Their results demonstrated that it is possible to
establish a correlation between stress resistance
and fermentation capacity.
The amount of available nitrogen is considered to be one of the main limiting factors for
yeast growth in musts (reviewed in Pretorius,
2000). Studies performed with wine yeasts
have generally found high expression levels
for genes linked to amino acid and purine
biosynthesis (Backhus et al., 2001; Cavalieri
et al., 2000; Hauser et al., 2001), which are indicative of high growth rates. Activation of the
methionine biosynthesis pathway and alterations in sulfate and nitrogen assimilation are
known markers for metabolic phenotype as
they are connected with cell-cycle progression
(Patton et al., 2000). The effect of nitrogen availability on the growth of wine yeasts has been
analyzed in two recent studies. One of these
compared global gene expression profiles in
synthetic media containing high and low
concentrations of arginine (a source of nitrogen)
(Backhus et al., 2001), whereas the other
compared expression profiles in a Riesling
must with normal concentrations of nitrogen
and another to which diammonium phosphate
(DAP) was added during the late fermentation
phase, when yeast growth is no longer active
(Marks et al., 2003). In the first study, it was
found that nitrogen limitation induced genes
that would normally be repressed by the high
concentrations of glucose in the must. This
suggests that, in the growth conditions that
characterize the fermentation of must containing high concentrations of sugars and nitrogen,
the use of glucose might be diverted, at least
partly, to a respiratory metabolism (Backhus
et al., 2001). This effect would be similar to
what is known as the Pasteur effect, which is
the inhibition of fermentation in the presence
of oxygen. Although this effect has been
reported to be irrelevant for yeast in laboratory
growth conditions (Lagunas, 1986), it might
occur in the fermentation of musts with low
levels of nitrogen, and, accordingly, cause sluggish or stuck fermentations. Indeed, it is standard practice in wineries to add DAP in such
cases. A study by Marks et al. (2003) found
that the addition of DAP affected the expression
of 350 genes. The 185 genes that were found to
be downregulated encoded small-molecule
transporters and nitrogen catabolic enzymes,
including enzymes involved in the synthesis of
urea, which is a precursor of ethyl carbamate.
The other 165 genes affected were all upregulated. These included genes involved in the
biosynthesis of amino acids, purines, and ribosomal proteins (suggesting a more active metabolism despite an absence of cell proliferation)
THE USE OF S. CEREVISIAE AS A MODEL ORGANISM FOR THE DEVELOPMENT
and assimilation of inorganic sulfate (necessary
for the elimination of hydrogen sulfide). The
results of the study by Marks et al. provide
a possible explanation for why the addition of
DAP reduces the production of ethyl carbamate
and hydrogen sulfide, two undesirable components in wines. They are also consistent with
results from a study that analyzed samples
taken at different time points during fermentation of a synthetic must with a relatively low
level of nitrogen (300 mg/L). The authors
reported that the gene expression pattern
observed could be explained by entry into the
stationary phase (cell proliferation arrest) in
response to nitrogen depletion; they also
reported that the process was regulated by the
TOR pathway (Rossignol et al., 2003).
A more comprehensive and realistic study
of transcriptional response in S. cerevisiae to
different nitrogen concentrations during alcoholic fermentation was published more recently
(Mendes-Ferreira et al., 2007a, 2007b). The
authors, using real grape must, compared 11
samples from different time points of a series of
control vinifications, nitrogen-limiting fermentations, and fermentations to which DAP was
added. They found alterations in approximately
70% of the yeast transcriptome in at least one of
the fermentation stages and also showed a clear
association between these changes and nitrogen
concentrations. In agreement with earlier findings published by Backhus et al. (2001), their
results indicated that early response to nitrogen
limitation involved the induction of genes associated with respiratory metabolism and a subsequent general decrease in the levels of genes
associated with catabolism. Curiously, they
also found a slight increase in the expression
level of genes encoding ribosomal proteins
and involved in ribosome biogenesis during
nitrogen depletion. In total, 36 genes were found
to be overexpressed when nitrogen levels were
low or absent compared to when DAP was
added. These signature genes might be useful
for predicting nitrogen deficiency and detecting
155
sluggish or stuck fermentations (Mendes-Ferreira et al., 2007b). The study also demonstrated
that the main transcriptional effect of adding
nitrogen was an upregulation in genes involved
in glycolysis, thiamine metabolism, and energy
pathways (Mendes-Ferreira et al., 2007a), findings that are similar to those reported by Marks
et al. (2003) following DAP addition. A study
performed by Jiménez-Martı́ and del Olmo
(2008) showed that the effect of nitrogen refeeding depended on the source of nitrogen used,
as they detected differences in gene expression
reprogramming depending on whether ammonia
or amino acids were added. The addition of
ammonia resulted in higher levels of genes
involved in amino acid biosynthesis, whereas
that of amino acids directly prepared cells for
protein biosynthesis.
Global gene response has also been analyzed
in low-temperature winemaking conditions,
which are widely considered to improve the
sensory quality of wine. In experiments carried
out at 13 and 25 C, Beltrán et al. (2006) observed
that the lower temperature induced cold stressresponse genes at the initial stage of fermentation and increased levels of genes involved in
cell cycle, growth control, and maintenance in
the middle and late stages of fermentation.
Furthermore, several genes involved in mitochondrial short-chain fatty acid synthesis were
found to be overexpressed at 13 C compared
to 25 C. These transcriptional changes were
correlated with higher cell viability, improved
ethanol tolerance, and increased production of
short-chain fatty acids and associated esters.
The natural environment of S. cerevisiae has
shaped the evolution of this organism’s metabolism to allow it to exploit the anaerobic conditions and high ethanol levels that characterize
fermentation and to tolerate high levels of
certain compounds that are common during
alcoholic fermentation. All these situations,
however, are causes of stress for S. cerevisiae
and are reflected in the yeast’s gene expression
pattern, even though the organism is capable
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6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
of responding effectively to these stresses. As
has already been discussed, differential expression of certain stress-response genes has been
detected in wine yeasts. The expression levels
of genes involved in oxidative metabolism, for
example, are low (Backhus et al., 2001). The
results of the fermentation monitoring study
conducted by Rossignol et al. (2003) indicate
that anaerobic stress is a characteristic of wine
fermentation and that the absence of ergosterol
synthesis, one of the main growth-limiting
factors for yeasts in musts with low oxygen
and high ethanol levels (see Pretorius, 2000),
is due to the continuous decrease in the expression levels of genes involved in ergosterol
biosynthesis.
Ethanol stress is another major pressure that
S. cerevisiae has to deal with during vinification.
Ethanol tolerance is not fully understood (Pretorius, 2000) but it is known to partly depend
on alterations in the plasma membrane. Genes
encoding enzymes involved in the synthesis
of fatty acids, phospholipids, and ergosterol
are highly expressed (Backhus et al., 2001) in
S. cerevisiae yeasts but decrease towards the
stationary phase (Rossignol et al., 2003). Using
microarray analysis to identify target genes
and analyze ethanol sensitivity in knockout
strains, Hirasawa et al. (2007) found that the
biosynthesis of tryptophan can confer ethanol
tolerance. Ethanol stress, however, does not
appear to be the main pressure in vinification.
The greatest effect on gene expression is
produced upon entry into the stationary phase
(Rossignol et al., 2003). The changes in gene
expression seen in this phase, however, appear
to differ from those observed under laboratory
conditions (Gasch et al., 2000).
In a comprehensive study of the transition
from the exponential to the stationary phase in
wine fermentation, Marks et al. (2008) discovered 223 genes that were dramatically induced
at various points during fermentation. They
called this the “fermentation stress response”
(FSR). The most interesting point was that the
FSR was found to overlap only partially with
the ESR (Gasch et al., 2000). Interestingly, 62%
of the FSR genes were novel, suggesting that
the stress conditions in wine fermentation
were rather different from those observed in
laboratory conditions. Also of interest was the
fact that respiratory and gluconeogenesis genes
were expressed even in high glucose concentrations and that ethanol accumulation, at least in
the experiment by Gasch et al., was the main
reason for entry into the stationary phase.
Because compounds such as copper sulfate
and sodium bisulfate have been used for many
years to inhibit fungal and bacterial growth on
vines and grapes and in wines, wine strains
might very well respond more efficiently than
other strains to these stresses thanks to the overexpression of certain detoxifying genes. Indeed,
wine strains have been found to overexpress
genes involved in the transport of sulfur
(SUL1-2) and sulfite (SSU1) (Cavalieri et al.,
2000; Hauser et al., 2001). It can be concluded
that the pressures to which wine strains have
been exposed over thousands of years have
led to the selection of strains that are better
adapted to the fermentation conditions found
in wineries. Strains that have developed resistance to treatments such as copper sulfate and
sodium bisulfate are a good example of this
adaptation.
Finally, two studies have analyzed the
genomic response in a commercial wine yeast
strain to rehydration and adaptation to osmotic
stress at the beginning of vinification. In the first
study, rehydration was carried out in a complete
glucose medium to identify events related to
re-establishment of fermentation (Rossignol
et al., 2006). The authors reported substantial
transcriptional changes. The expression profile
observed in the dried yeasts was characteristic
of cells grown under respiratory conditions
and exposed to nitrogen and carbon starvation
and considerable stress during rehydration.
Furthermore, many genes involved in biosynthetic pathways (transcription or protein synthesis)
THE USE OF S. CEREVISIAE AS A MODEL ORGANISM FOR THE DEVELOPMENT
were coordinately induced while those subject
to glucose repression were downregulated.
While expression of general stress-response
genes was repressed during rehydration,
despite the high sugar levels, that of acid-stress
genes was induced, probably in response to the
accumulation of organic acids. In the second
study, rehydration was carried out in water to
separate this process from adaptation to osmotic
pressure (Novo et al., 2007). The results of the
study showed that rehydration for an additional
hour (following an initial period of 30 min) did
not induce any relevant changes in global gene
expression. The incubation of rehydrated cells
in a medium containing fermentable carbon
sources activates genes involved in the fermentation pathway, the nonoxidative branch of the
pentose phosphate pathway, ribosomal biogenesis, and protein synthesis.
Erasmus et al. (2003) analyzed yeast response
to high sugar concentrations by inoculating
rehydrated wine yeast in Riesling grape juice
containing equimolar amounts of glucose and
fructose to a final concentration of 40% (wt/
vol) and comparing global gene expression
with that observed in yeasts inoculated in the
same must containing 22% sugar. Although
the sugar concentration used is not generally
found in winemaking conditions, some of the
results coincided with those reported by Rossignol et al. (2003), with sugar stress resulting in
the apparent upregulation of glycolytic and
pentose phosphate pathway genes and structural genes involved in the formation of acetic
acid from acetaldehyde and succinic acid from
glutamate and the downregulation of genes
involved in the de novo biosynthesis of purines,
pyrimidines, histidine, and lysine. The authors
also reported considerable changes in the
expression levels of stress-response genes.
These changes affected, among others, genes
involved in the production of the compatible
osmolyte glycerol (GPD1) and genes encoding
the heat shock proteins HSP104/12/26/30/42/
78/82 and SSA3/4.
157
Gene expression profiling of industrial
strains may also help to uncover as-yetunknown functions of numerous genes in the
S. cerevisiae genome, as these genes might only
have relevant functions in industrial fermentation conditions. For instance, 130 genes from
various subtelomeric families of unknown function (PAU, AAD, COS) have been found to be
induced during wine fermentation (Rossignol
et al., 2003), indicating that they probably have
an important role in this process. It should
also be noted that 28% of the FSR genes detected
in the experiment by Marks et al. (2008)
described above had an unknown function.
4.4. Genomic Studies
DNA microarray analysis is also a promising
tool for the study of wine strain genomes. This
technology forms the basis for various types of
study in this area, including Affymetrix oligonucleotide microarray analyses. These microarrays consist of a very large number of short
oligonucleotide sequences derived from the
reference S. cerevisiae laboratory strain S288c.
The oligonucleotides represent all the open
reading frames (ORFs) distributed throughout
the yeast genome. In this method, hybridization
is highly dependent on the identity of the
sequence, and a single nucleotide change will
alter the hybridization signal. Thus, the signals
produced by a particular strain can be
compared with those from a reference strain to
identify sequence changes, including SNPs.
The method has been successfully used to study
polymorphisms in various strains (Primig et al.,
2000; Winzeler et al., 1998). Affymetrix also
manufactures tiling arrays, another type of
oligonucleotide microarray system that covers
the entire sequence of the yeast genome. Tiling
arrays are used for transcriptome mapping
and to identify transcripts that do not correspond to annotated genes (Royce et al., 2005).
These arrays have also been used for detailed
genomic analysis. As described in Section 3,
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6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
Schacherer et al. (2009) used this method to resequence 63 yeast strains, including 14 wine
strains.
There also exist tiling arrays with long oligonucleotides (manufactured by Agilent, for
example) and arrays containing probes spotted
at a lower density than that seen in tiling arrays
(oligonucleotides over 60 bases long or doublestrand fragments of 300 or more bases). These
tools, however, are not suitable for detecting isolated sequence variations. Microarrays consisting of long oligonucleotides or double-strand
fragments are, however, useful for genomic
comparisons designed to identify increases or
decreases in the number of copies of a particular
gene or chromosomal region. The first study of
this type was conducted by Hughes et al.
(2000b) using laboratory strains. A similar study
by Infante et al. (2003) that analyzed S. cerevisiae
flor yeast strains found that two natural strains
had differences in the copy number of 38% of
their genes, which illustrates the enormous
genomic variability that characterizes yeasts of
this type. In many cases, the differences were
in regions flanked by Ty transposons and other
regions with a high recombination rate, which
would explain the amplification or deletion
events observed. The authors suggested that
such regions were the site of double-strand
breaks responsible for free ends capable of
recombination with short homologous regions
(10e18 base pairs). A similar mechanism has
been described for the SSU1 gene region in
wine strains (see Section 4.3). In the case of flor
yeast strains, the continuous presence of acetaldehyde and ethanol in the medium would
increase the frequency of double-strand breaks,
conferring a selective advantage on strains that
have adapted to this hostile environment.
DNA macroarray analysis has also been used
to study gross gene expression profiles in the T73
wine strain (Pérez-Ortı́n et al., 2002b). The study
revealed numerous copy-number variations for
genes from subtelomeric families and a number
of other genes such as the copper resistance
gene CUP1. Curiously, CUP1 has a deletion in
the genomic region of the wine strain (PérezOrtı́n et al., 2002b), which reduces its expression
levels (Hauser et al., 2001). The study by Hauser
et al. found that the number of Ty transposons
(Ty1, Ty2, Ty3, and Ty4) was greatly reduced
in the T73 wine strain compared to the S288c
laboratory strain. This finding was consistent
with less-complete previously published results
(Jordan & McDonald, 1999), with later results
(Carreto et al., 2008), and with results for brewing
strains (Codón et al., 1998) and suggests that the
colonization of the genome of laboratory strains
by these molecular parasites may be recent. The
strong selective pressure exerted on wine strains
might have prevented the excessive accumulation of sequences of this type (Jordan &
McDonald, 1999).
The flexibility of DNA chip technology
means that purpose-designed arrays can be
created for specific studies. In a study of chromosomal rearrangements in Cava strains
(secondary fermentation), Carro et al. (2003)
used specially designed and constructed macroarrays containing 14 chromosome I probes and
hybridized them with DNA from chromosome
I isolated from various Cava strains with length
variations in this chromosome. Their results
indicated the existence of a subtelomeric region
that tends to be deleted in the right arm of chromosome I of this highly variable strain.
5. PROTEOMIC ANALYSIS OF WINE
STRAINS
DNA microarray technology allows the
expression of all the genes in a particular
organism (the transcriptome) to be analyzed.
Global analyses can thus be used to assess the
effects of physical, chemical, and biological
agents, and even specific mutations, on gene
expression. Nonetheless, analysis of mRNA
levels is not sufficient for a complete description
of biological systems. This also requires accurate
159
PROTEOMIC ANALYSIS OF WINE STRAINS
FIGURE
6.3 Standard
proteomic analysis by twodimensional (2D) gel electrophoresis
and
mass
spectrometry (MS). The
method consists of three
fully integrated steps. In
the first step, the proteins
are separated on 2D gels,
stained, and then individual spots isolated. The
protein spots are then
digested with trypsin and
the resulting peptides are
separated by high-performance liquid chromatography (HPLC). In the
second step, each eluted
peptide is ionized by electrospray ionization. It then
enters the mass spectrometer through the first
quadrupole mass filter (Q1)
and is fragmented in
a collision cell (Q2). The
resulting
spectrum
is
recorded (Q3). In the third
step, the tandem MS spectrum of a selected ionized peptide contains sufficient specific sequencing information to identify the peptide and its
associated protein. m/z ¼ mass to charge ratio.
measurement of the expression and activity of
the corresponding proteins (the proteome).
Furthermore, even though expression levels of
different mRNA species and the proteins they
encode are correlated, this correlation is not
perfect for all genes (Futcher et al., 1999; Ideker
et al., 2001). Of even greater importance,
however, is the level of correlation between
changes in mRNA and protein levels. While
changes in the proteome and transcriptome
generally occur in parallel (homodirectional
changes), the multiple effects caused by posttranscriptional regulation justify the need for
proteomic studies (Griffin et al., 2002; Ideker
et al., 2001). Thus, proteomics, which is the analysis of the full complement of proteins
expressed by a genome (Pennington et al.,
1997; see Figure 6.3), is considered to be the
best tool for obtaining a quantitative description
of the state of a biological system. In other
words, proteome analysis provides a better
picture of an organism’s phenotype than does
the analysis of mRNA levels.
While there are vast amounts of genomic
data available for yeasts (including sequence
and gene expression data obtained by DNA
microarray analysis), the yeast proteome is
still largely undefined (Fey et al., 1997). This
is particularly true for yeasts of industrial
and biotechnological interest, as most of the
studies to date have analyzed laboratory
strains (Link et al., 1999; Washburn et al.,
2001). The first comparative study in this
area, performed using three haploid strains
derived from laboratory strains, led the authors
to conclude that differences in protein expression level and post-translational modifications
influenced the molecular and biochemical
160
6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
characteristics of cells and were possibly
responsible for the different mutant phenotypes observed in these strains (RogowskaWrzesinska et al., 2001).
Several studies have analyzed the effect of
environmental stresses on proteome-level
responses in laboratory strains. These studies
are similar to those conducted in the area of
genomics analyzing the influence of environmental factors on global gene expression in
laboratory strains. One such proteomic study
analyzed oxidative stress caused by hydrogen
peroxide (Godon et al., 1998) leading to the
expression of batteries of genes referred to by
the authors as “stimulons.” The expression of
115 proteins with different functional roles was
observed. These included proteins linked to
antioxidant activity, heat shock response, and
protease activity. The expression of 52 proteins,
including metabolic enzymes and proteins
involved in translation, was repressed. In
another study of S. cerevisiae, sorbic acid was
found to produce slightly different and less
drastic effects, although it did reveal expression
of stress-response proteins (mainly linked to
oxidative stress) and several molecular chaperones (Hsp12, 26, 42, and some isoforms of
Hsp70) (de Nobel et al., 2001). Analysis of
mRNA levels following the induction of sorbic
acid stress showed that these were poorly correlated with protein abundance.
In another proteome analysis, the addition of
cadmium (Cdþ2) induced expression of 54
proteins and repressed that of a further 43
(Vido et al., 2001). Of these, nine enzymes
involved in the sulfur amino acid biosynthesis
pathway and glutathione (GSH) synthesis
were strongly induced, as were proteins with
antioxidant activity. Although Cdþ2 is not an
active redox ion, it can cause oxidative stress
and lipid peroxidation and also affect cellular
thiol redox balance. These data suggest that
the two cellular thiol redox systemsdGSH and
thioredoxindare essential protection mechanisms against cadmium stress, a theory later
corroborated by Fauchon et al. (2002), who
related cadmium stress with sulfur metabolism.
As GSH is essential for the detoxification of
cadmium, when exposed to this substance, cells
convert most sulfur into GSH. The cells change
their proteome to reduce the production of
sulfur-rich proteins to permit optimal GSH
turnover and ensure optimal levels of this essential compound. It has been estimated that this
change allows for a 30% reduction in sulfur
amino acid incorporation into proteins, which
would enable a considerable increase in GSH
production and thus ensure cell survival. This
is a clear example of the important role of proteome plasticity in yeast cell adaptation to
adverse conditions and agents.
Little information is available on the proteomic profiles of industrial yeasts as most of the
studies in this area have been carried out using
laboratory strains. In two studies involving the
analysis and identification of over 200 proteins,
Joubert et al. (2000, 2001) concluded that the K11
brewing strain was a hybrid of S. cerevisiae and
Saccharomyces pastorianus (S. bayanus). Their
work also led them to postulate that the physiological properties required by top-fermenting
(ale) strains (flocculation and fermentation at
low temperatures) might have been acquired
by hybridization. Their reasoning was based
on the fact that, unlike bottom-fermenting
(lager) strains, which are all hybrids, top-fermenting strains are not hybrids and are very
closely related to S. cerevisiae laboratory strains.
The two types of brewing strain also have very
different physiological properties.
Trabalzini et al. (2003) studied the proteomic
response in a wine strain of S. cerevisiae (k310)
isolated during spontaneous wine fermentation.
Wine strains are exposed to numerous hostile
conditions during fermentation. Unlike other
studies, which have analyzed isolated effects of
environmental stress on yeasts, the study by
Trabalzini et al. investigated physiological
response to fermentation stress; in particular,
depletion of the main carbon source and glucose,
OTHER GLOBAL STUDIES
and increasing ethanol levels. They found that
specific proteins, which differed from those
observed for other S. cerevisiae strains (such as
those used in bread making), were either
induced or repressed in response to these physiological stresses. The proteomic response also
involved the induction of intracellular proteolysis, which appeared to be directed towards
certain classes of protein. The main inference
from this study is that the proteomic response
to fermentation stress in a wine strain of S. cerevisiae is largely directed at mitigating the effects
of increasing ethanol levels. Ethanol stress has
been associated with both oxidative damage
(due to an increased production of free radicals)
and cytotoxic effects (due to acetaldehyde
production). Ethanol also induces the expression
of heat shock proteins and proteins involved in
trehalose metabolism, whose purpose is to stabilize membranes and proteins and suppress
protein aggregation. It is extremely important
to further investigate proteomic responses in
fermentation yeasts as a good wine strain must
be capable of overcoming the hostile conditions
it is faced with in industrial processes. Additionally, the cell changes that occur in S. cerevisiae
during fermentation (autoproteolysis) and aging
(autolysis) are responsible for the organoleptic
properties of wine. Accordingly, the amount
of nitrogen in autolysates together with free
amino acid concentrations, which differ greatly
depending on the yeast strain, can have a considerable influence on the flavor, composition, and
quality of the final product (Martı́nez-Rodrı́guez
et al., 2001a, 2001b). Proteolytic enzymes might
be involved in the turnover of nitrogenous
compounds before and during autolysis in winemaking conditions. It has also been proposed
that yeasts might use amino acids not only as
sources of nitrogen but also to restore the redox
balance in critical environmental conditions
(Mauricio et al., 2001).
Two recent studies have compared the transcriptome and proteome of wine yeasts. In the
first of these, Zuzuárregui et al. (2006) compared
161
two wine strains with different fermentative
capacities and found that one of the strains
was incapable of completing fermentation.
Although the transcriptome and proteome analyses revealed specific differences, they both
indicated that the strain with fermentation difficulty had defects, namely excess proton uptake
(a sign of ethanol intolerance) and increased
oxidative damage due to elevated levels of acetaldehyde. In the second study, Rossignol et al.
(2009) compared proteomic changes in a wine
strain between the exponential growth phase
and the stationary phase during wine fermentation. They found major changes in the abundance of proteins related to glycolysis, ethanol
production, and amino acid metabolism. The
most interesting finding was that these changes
were very poorly correlated with previously
observed transcriptional changes (Rossignol
et al., 2003), which suggests that post-transcriptional regulatory mechanisms are very important in the late stages of wine fermentation. A
recent study involving laboratory strains and
laboratory culture conditions with various
nutrient deficiencies indicated that the response
to nitrogen depletion was fundamentally
controlled at a translational and not a transcriptional level (Kolkman et al., 2006).
The importance of gaining a comprehensive
understanding of proteomic response in
fermentation yeasts is thus clear: it will greatly
contribute to improving the organoleptic properties associated with high-quality wines.
6. OTHER GLOBAL STUDIES
One of the aims of large-scale studies is to
provide a global view of living systems. Genomics, for example, focuses on the full genome
to help understand the relevance of individual
genes, while transcriptomics and proteomics
analyze the link between physiological changes
and changes in transcript and protein expression levels with respect to total RNA or protein
162
6. GENOMIC AND PROTEOMIC ANALYSIS OF WINE YEASTS
expression levels. Most of the large-scale functional studies conducted to date have been
based on transcriptomic and proteomic analyses. A more recent “omic” approach, metabolomics, aims to characterize the physiological
state of a cell by determining the concentration
of all of the small molecules that comprise the
metabolism and identifying metabolic pathways and fluxes. This approach may provide
the best and most direct measurement of an
organism’s physiological activity and bring us
a little closer to a true approximation of its
phenotype since, as stated by Delneri et al.
(2001), “mRNA molecules are not functional
entities within the cell, but simply transmitters
of the instructions for synthesising proteins.
proteins and metabolites [in contrast] represent
true functional entities within cells” (p. 87).
Furthermore, the use of metabolomic data in
the systematic analysis of gene function has
the added advantage that there are considerably
fewer metabolites than genes or gene products.
Nevertheless, unlike proteins, metabolites are
not directly related to genes.
Metabolomic studies have emerged in an
attempt to assign functions to genes on the basis
of metabolic analyses. The primary aim is to
discover biochemical reactions catalyzed by
enzymes encoded by genes of unknown function (Martzen et al., 1999). The difficulty with
such an approach is that it assigns mechanisms
rather than biological functions.
An alternative approach would be to study
changes in the metabolome induced by the deletion or overexpression of a specific gene and to
then assign functions by comparing the changes
induced with those observed in similar manipulations of known genes. Such an approach,
referred to as metabolic footprinting, was used
by Raamsdonk et al. (2001) in S. cerevisiae.
Measuring concentrations of specific metabolites in a cell, however, is a very costly process.
The approach used by Raamsdonk et al. was
extended in a subsequent study by the same
group (Allen et al., 2003) to permit large-scale
analyses by optimizing the experimental conditions and surmounting the technical difficulty of
measuring intracellular metabolites, which have
a rapid turnover and need to be separated from
the extracellular space. The optimization of mass
spectrometry has allowed the analysis of extracellular metabolites in spent culture medium.
It is also possible to study and define specific
metabolic pathways by integrating and incorporating data obtained using the technologies discussed in this chapter into biological models to
predict cell behavior that can then be tested
experimentally. Ideker et al. (2001), for example,
used a combined genomic and proteomic
approach to elucidate the galactose utilization
metabolic pathway. They followed a typical
strategy used in systems biology. The steps
they described are summarized in the following
points: (1) definition of all the genes in the
pathway of interest; (2) perturbation of each
pathway component through a series of genetic
or environmental manipulations and quantification of global cellular response; (3) integration of
the observed mRNA and protein responses with
the current, pathway-specific model; and (4)
formulation of new hypotheses to explain observations not predicted by the model. Although
metabolomics is a relatively new field, a study
by Eglinton et al. (2002), using metabolomic
analysis of mutant laboratory strains, showed
how genetic modification affects the production
of several secondary metabolites of fermentation including acids (such as acetic acid), esters,
aldehydes, and higher alcohols. Many of these
metabolites make an important contribution to
the flavor and aroma of the wine. A recent study
by Rossouw et al. (2008) investigating the relationship between the transcriptomes of five
wine strains and the aroma profile produced
during fermentation found that the expression
levels of five genes were related to differences
in aroma. They then constructed wine strains
overexpressing these genes and found that the
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to the predicted changes.
7. FUTURE DIRECTIONS
The use of genomic and proteomic methods to
study wine yeasts is still in its infancy. Although
the results achieved so far have begun to provide
molecular explanations to problems related to
wine yeast physiology, we are still far from the
level of detail available for laboratory strains.
It is important to discover what makes
wine strains capable of must fermentation in
circumstances in which the much-better-known
laboratory strains are not. Laboratory strains of
S. cerevisiae are indeed nothing more than simplified genomic derivatives of natural strains. Deciphering the genome of wine strains is also
interesting from a basic scientific perspective.
Gaining a deeper understanding of the genome,
transcriptome, and proteome of wine yeasts and
integrating this information into mathematical
models capable of predicting physiological
changes will allow carefully constructed
improvements in the characteristics of these
strains and the biotechnological processes in
which they participate.
Although we have been making wine for
over 7000 years, we only very recently discovered, thanks to Louis Pasteur, that S. cerevisiae
was the main driving force behind the process.
Since then, this yeast has been the focus of
much basic and applied research. Nowadays,
the in-depth information that large-scale studies
can provide on the full complement of macromolecules found in this microorganism will
help us to fully understand its physiology and
elucidate the manner in which it makes wine.
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